Methods and systems architecture to virtualize energy functions and processes into a cloud based model

ABSTRACT

A system for creating an energy performance and predictive model. The system includes a non-transitory computer-readable storage medium which performs the steps obtaining parametric information objects that represent actual physical objects and modifying the parametric information objects by embedding data related to energy performance characteristics unique to the device represented. The system further performs the steps grouping the modified parametric information objects that define actual real world interrelationships to create a complete virtualized project and parsing the virtualized model data set to create a first parsed data set and a second parsed data set. The first parsed data set creates the project system control application, which acts upon and coordinates the actions of the real device through the virtual field bus. The second parsed data set creates the project&#39;s virtualized energy performance project and represents the subset of the virtualized performance environment where other virtualized devices can act upon it.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 14/203,576, filed Mar. 11, 2014 which claims the benefit of andpriority to U.S. Provisional Patent Application Ser. No. 61/784,100filed on Mar. 14, 2013, which application is incorporated herein byreference in its entirety.

BACKGROUND OF THE INVENTION

The current US electrical energy grid architecture is inefficient.According to the DOE, 45 percent of energy produced by the electric gridgenerators, on a BTU basis, is lost. A major portion of this power isgenerated by fossil fuels and millions of metric tons of carbonemissions are released into the atmosphere annually to support thismassively inefficient and antiquated centralized electrical gridarchitecture.

In 1996, under Federal Energy Regulatory Commission (FERC) Order 888,the establishment of independent service operators (ISO's) began theprocess of recognizing the separation of generation, transmission, andpower distribution into separate entities that exist to transition theelectrical power grid into open electric grid market functions. This wasfollowed up by FERC Order 2000 in 1999 for directing open wholesaleelectrical markets. Similar trends have occurred in the natural gasdelivery markets. Since the inception of wholesale markets, the marketmodel has placed pooling of generations sources and dispatching ofresources across the grid in a transparent and unbiased way, allowingfor market pricing and regional needs to drive market demand pricing.The current legacy grid model does not distinguish between generationssources, identify environmental effects, inherent losses that lead toinefficiencies and unaccounted for impact costs. Further, until theintroduction of smart meters, grid operators had very little granularinformation about what was happening at the point of dispatched powerand this was a problem. As a result of alternate energy sources such aswind and solar and as the grid became interconnected and diversified,more problems were encountered with fluctuations outside the control oftraditional legacy grid operators Currently, grid operators establishpredictions on consumption levels for major demand response events andthen reconcile days later the information and how various operatorscontributed to the demand response event. As grid operators havestruggled with modeling the generational sources and their interrelationchallenges with consumption, it is clear a comprehensive model of sourceand consumption is needed.

Parallel to electrical market deregulation, DOE, FERC and EPA asdirected by Presidential Executive Order 13624 through the use ofcombined heat and power (CHP) have identified a concept where waste heatgenerated from any process should be considered as a source to generateelectricity, which by its very nature places electricity generationcloser to the point of consumption as being more efficient with a lowoverall impact to the environment. The distribution and utilization offossil fuels, in particular natural gas, with all of its benefits as asecond source of local electrical generation has not been integratedinto an overall energy solution. It is also necessary to look at fossilfuels with the ability to segregate traditional combustion of fuels togenerate electricity that create higher levels of greenhouse gases fromfuel cells that produce electricity more efficiently with lower CO2impact.

Recent hurricanes and natural disasters have shown the weakness inreliability of aging, large legacy grid systems and the need for adistributed grid architecture. This coupled with NetZero developmenttrends predict a new distributed electric power grid architecture fornew infrastructure and upgrade plans for existing grid replacements.This invention lays out an architecture where all market forces areaccounted for in real time with real time market exchange, whileallowing grid operators the freedom to interact with predictive andmultilevel tiered pricing structures.

This invention and its various embodiments is to model energy flow assource raw materials to power plants, power and heat generation andgenerational losses with by products, transmission/reactive losses,congestion/distribution processes and consumption elements and devicesin a virtualized cloud environment. The modeling environment is createdusing a compilation of virtualized parametric objects created ormodified within the parametric design process to represent real physicaldevices. These objects are then parsed to yield virtualized energyelements that interact with both consumption and generation models. Thesmallest unit of virtualized consumption is any process or entitydirectly or indirectly requesting any form of energy. Energy sources arevirtualized with a full attribute structure including source, heatcontent, greenhouse gases (GHG), water, transmission distance lossesrelative to the individual consumer. And as the virtualized model beginsto account for all associated costs incurred by legacy electricalgeneration (ISO's), normalized costing across all metrics will driveinnovation in areas of distributed electrical grid architecture andcarbon sequestration in an open market environment.

BRIEF SUMMARY OF SOME EXAMPLE EMBODIMENTS

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential characteristics of the claimed subject matter, nor is itintended to be used as an aid in determining the scope of the claimedsubject matter.

One example embodiment includes a system for creating an energyperformance and predictive model. The system includes a non-transitorycomputer-readable storage medium including instructions that, whenexecuted by a computing device, cause the system to create virtualizedenergy objects that represent physical systems, characteristics, andperformance by performing the steps obtaining parametric informationobjects that represent actual physical objects and modifying theparametric information objects by embedding data related to energyperformance characteristics unique to the device represented. The systemfurther performs the steps grouping the modified parametric informationobjects that define actual real world interrelationships to create acomplete virtualized project and parsing the virtualized model data setto create a first parsed data set and a second parsed data set. Thefirst parsed data set creates the project system control application,which acts upon and coordinates the actions of the real device throughthe virtual field bus. The second parsed data set creates the project'svirtualized energy performance project and represents the subset of thevirtualized performance environment where other virtualized devices canact upon it.

A virtual thermostat for virtualizing energy functions and processes.The virtual thermostat includes a sensor array. The sensor arrayincludes an interior temperature sensor at a thermostat location, wherethe interior temperature sensor is configured to measure the temperaturewithin an interior space. The virtual thermostat also includes anoutdoor temperature sensor, where the outdoor temperature sensor isconfigured to measure the temperature sensor of an outdoor location inproximity to the interior space. The virtual thermostat further includesa supply air temperature sensor, where the supply air temperature sensoris configured to measure the temperature of conditioned air that isbeing supplied to the interior space. The virtual thermostatadditionally includes one or more computer readable media, where the oneor more computer readable media contain a set of computer-executableinstructions to be executed by the logic device. The set ofcomputer-executable instructions configured to receive input from thesensory array and create a unique representation of the physical systemscontrolled. The set of computer-executable instructions also configuredto create a unique data set of the virtualized systems and connect thisdata set to other networked devices using machine to machine interactionand allow the virtualized thermostat to share simple user data and userrequests with other consumption or generational models. The set ofcomputer-executable instructions further configured to allow the dataset representing the virtualized thermostat to exist outside of thecontrolled system and employ sophisticated control logic/algorithms tooutput one or more control signals designed to affect the desiredconditioning.

These and other objects and features of the present invention willbecome more fully apparent from the following description and appendedclaims, or may be learned by the practice of the invention as set forthhereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

To further clarify various aspects of some example embodiments of thepresent invention, a more particular description of the invention willbe rendered by reference to specific embodiments thereof which areillustrated in the appended drawings. It is appreciated that thesedrawings depict only illustrated embodiments of the invention and aretherefore not to be considered limiting of its scope. The invention willbe described and explained with additional specificity and detailthrough the use of the accompanying drawings in which:

FIG. 1 illustrates the parametric to virtualized object flow chart;

FIG. 2 illustrates a virtualized model consisting of a static anddynamic component;

FIG. 3 illustrates a virtualized consumption model;

FIG. 4 illustrates a virtualized generational model;

FIG. 5 illustrates the virtualized environment;

FIG. 6 illustrates the transactional flow chart;

FIG. 7 illustrates the virtual thermostat components;

FIG. 8 illustrates a radiant/thermal sensor; and

FIG. 9 illustrates an example of a suitable computing environment inwhich the invention may be implemented.

DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS

Reference will now be made to the figures wherein like structures willbe provided with like reference designations. It is understood that thefigures are diagrammatic and schematic representations of someembodiments of the invention, and are not limiting of the presentinvention, nor are they necessarily drawn to scale.

Definitions:

“Parametric Virtualized Object”—The compilation of objects created ormodified within the parametric design process (3D design, BuildingInformation Modeling (BIM)) comprised of parametric attributes,metadata, or some other machine readable code that is the basic unit tothe virtualized environment. With respect to this invention, thevirtualization of objects have specific parametric data included thatrepresent the Object's energy and performance characteristics that arecommon and standardized across the entire virtualized environment.

“Bayes Theorem”—Gives relationship probabilities of A and B, P(A) andP(B), and the conditional probabilities of A given B and B given Awhere: [0025] P(A|B)=P(B/A)*P(A)/P(B); where the degree of probabilityin a proposition before and after accounting for evidence. With respectto this invention, we use Bayesian interpretation to make predictionswithin the dynamic model and pinpoint performance issues when comparedto the actual results.

“Cloud Computing”—A phrase used to describe a variety of computingconcepts that involve a large number of computers connected through areal-time communication network such as the Internet. In science, cloudcomputing is a synonym for distributed computing over a network, andmeans the ability to run a program or application on many connectedcomputers at the same time.

“Virtualized User”—The smallest unit of virtualized consumption. Thesmallest unit would be a virtualized representation of a single plug-inappliance, the estimated energy consumed to process a transaction, orany entity directly or indirectly requesting any form of energy.

“Virtualized Device”—A virtualized device is any software representationof a real physical component or entity that contain the same attributesand responds in the same way as the real device.

“Virtualized Consumer”—Any software representation of a person or being,an entity with attributes used to describe relationships withvirtualized elements.

“Virtualized Field Bus (VFB)”—A software representation of a data fieldbus containing input and output variables where some points of data mayinterface the virtualized data with real physical data.

“Virtualized Generational Element”—Any software representation of steps,processes, methods, or concepts in part or in total that createelectrical energy and/or heat.

“Ancillary Services”—Any method or representation that improves thedelivery of the utility grid which can include any physical apparatusattached to any portion of the grid or as virtualized portions of thegrid that are manipulated through software to improve, modify, oroptimize grid power factor or performance using anticipation,synchronization, or limiting algorithms.

“System Control Application”—The control algorithms, senor and deviceinterrelationships that allow for control and performance as intended bythe system designer.

This invention and its various embodiments is a virtualized model thatexists within cloud computing environment. The model is comprised ofobject oriented modules developed in a well-established softwareenvironment such as Java that represent both energy generation andconsumption within the virtualized environment.

In order to create the virtualized environment 100 for a givenconstruct, FIG. 1 illustrates parametric virtualized objects 103 withspecific information embedded within the object's attributes are joinedas a compilation of objects created or modified within the parametricdesign process (3D design, BIM). And as objects are inserted into theproject design model 104, parametric attributes, metadata, or some othermachine readable code, defines the basic relational organization for allvirtualized devices and forms the basis of the parametric design model.

The parametric design model can be created as part of an engineeredproject to describe a construction project 105, an existing projectconditions, or using any method available and a standardized templatewith the end product established as describing a virtualized energyconsumption or generation device.

The parametric design model includes virtualized consumption elements ordevices for control, management, life cycle costs, consumption priority,predictability, and strategies for energy consumption requests andpredictive energy usage.

The parametric design model includes virtualization of energygeneration, management, life cycle costs, availability, predictability,transmission, distribution, and consumption elements. Energy generationwill be virtualized into energy types, harvested at the source (coal,oil, natural gas, solar, hydroelectric, nuclear, biomass, etc.) andenergy consumption can be broken down into individual energy users.

Once the parametric design model is completed it is verified invirtualization mode to confirm the component performs as the actualdevice it represents, it is said to be characterized. Oncecharacterized, the parametric model undergoes two separate but parallelparsing operations.

The first parsing operation 106 extracts the project system controlapplication data from the parametric data contained within each object.This metadata, or some other machine readable code, defines the basicrelational organization and sequences for all devices within the realproject environment. Once this data is parsed and verified, it becomesthe basis of the dynamic model and the system control application. Usinguniform resource locator (URL), polymorphism, or a combination oflinking methods to relate and update in real time the parsed data to theoriginal parametric model, a 3D real time control and management tool iscreated.

The second parsing operation 109 extracts the virtualized energyperformance data from the parametric data contained within each object.This metadata, or some other machine readable code, defines avirtualized device within the static model to be described below. It isworth noting that the two parsed data sets are parsed differently: oneis parsed to create a data file used for the basis of a control platformand open connectors to the virtual field bus, the second is parsed tooperate independently in the virtualized environment and open connectorsto the predictive model data set.

One skilled in the art will appreciate that, for this and otherprocesses and methods disclosed herein, the functions performed in theprocesses and methods may be implemented in differing order.Furthermore, the outlined steps and operations are only provided asexamples, and some of the steps and operations may be optional, combinedinto fewer steps and operations, or expanded into additional steps andoperations without detracting from the essence of the disclosedembodiments.

As the virtualized environment develops, each device exists both as astatic model 201 and dynamic model 202 at each level of the construct asillustrated in FIG. 2. The portion of the virtual field bus that iscomprised of the actual data bus input 204 and output 207 is the onlyportion of the construct that resides at the consumption or generationdevice. Once these data points are mapped to the virtualized data bus asnormalized values, the rest of the model can be segregated acrossmultiple processors, differentiated, and stored as need across multipledata base platforms. The benefit should be obvious with the ability toread or write one variable across a large scaled deployment ofcomparable devices.

In order for the dynamic model to respond, it must first look at themodified variables 205 that it is presented and determine if the staticmodel 201 and the dynamic model 202 match in response. If the two modelsmatch, the output is written to the virtualized data bus output and sentout to affect change. If the two models do not match, the variationsbetween the static and dynamic model are reconciled and returned to thepredictive data interface 206. By the use of two virtualized independentmodels and synchronizing/comparing the results, it is possible toovercome the limit of processing a variable data set with second orderor higher equations or nested relational values in real time. The resultis the use of a static model to process equations and keep the interfaceto the dynamic model: a comparator value.

The static model 201 contains a predictive algorithm 206 based onBayes's Theorem to generate a user demand evaluation within theconsumption model and a predictive solution, based on the generationalmodel. Both models obtain field data 204 modified in real time, but onlythe dynamic model 202 can interact with the physical world at any pointwithin the model. The dynamic model issues write commands to virtualizeddevices that interact with actual field devices, and affect changeswithin each virtualized device.

As illustrated in FIG. 3, the smallest unit of the virtualizedconsumption model or module is the individual user 302, with itspreferences moving around within the consumption model 301. For eachconsumption device, there is a predictor block variable 303 thatallocates estimated consumption requirements, summarizes preferences andpasses this data back to the aggregated virtualized consumption.

As illustrated in FIG. 4, the smallest unit or module within thevirtualized generational model is the generational source, comprised ofall of its generational costs including environmental, source, capitalcosts and anticipated future costs for each source type, delivered toeach user's point of use within the virtualized grid. For eachgenerational device, there is a predictor block variable 403 thatestablishes a generational capacity along with cost point, environmentalcapping, cost point per source, and source type and this data is passedback to aggregated virtualized consumption within the virtualizedenvironment.

The current state of art for energy consumption and demand response isto allow utility grid operators to define a daily rate structure andtime of day demand events. These events and pricing are developed byindependent service operators (ISO's) on given generation capacity andanticipated demand consumption with final cost factors resolved at somelater date.

FIG. 5 represents the overall virtualized environment that bringstogether the market transaction level 501, which is the central exchangeand interface between the consumption 502 and generational 507 portionsof the system architecture manages two distinct computational paths: thestatic and dynamic model.

Within the virtualized consumption model, individual users 508 are givenpurchasing choices via web based tools to select the types of energysources and cost point.

Energy sources 504 are virtualized with a full attribute structureincluding source, greenhouse gases (GHG), water, transmission distancelosses to create normalized costing specific to the individual consumeror virtualized device. Premium sources of energy such a solar PV arecosted against the full acquisition cost of fossil fuels that includessource harvesting and carbon sequestration costs. Consumers will selectenergy sources based on personal eco objectives using a connecteddevice, web, or social networks that will drive change in personalbehavior. Once consumers are given choices and begin to drive the demandfor alternate, local, sustainable sources, the paradigm for generatorswill be switched from a demand reduction to a distributed, carbonneutral electrical grid architecture where consumers can be collective,cooperative, bundled, or agent representative and tracked in real time.

And as the virtualized model begins to account for all associated costsincurred by legacy electrical generation (ISO's) 505, normalized costingacross all metrics will drive innovation in areas of distributedelectrical grid architecture and carbon sequestration in an open marketenvironment. For example, California consumers will see theirconsumption of coal fired power generation in a distant State that stillallows air quality standards for California generators exceeded in realtime. States and grid operators that charge punitive rates will beidentified and market forces will drive new alternatives and options.

First Implementation—Thermostat

The first implementation of the virtualized model is the basicthermostat. In its simplest form, the thermostat can make heating andcooling decisions and control mechanical equipment. Past art has foundways to make the thermostat smarter and more user friendly. Further, thethermostat may be network connected with algorithms that respond toutility demand commands/rate structures, while trying to maintain usercomfort. I.e., it attempts to be more efficient without diminishing theuser's experience.

But the basic model is flawed for numerous reasons including the simplefact that a thermostat is located in the wrong place, typically near thereturn air to the mechanical system and this location has little to dowith occupant comfort. The latest research indicates that this placementof the thermostat leads to energy losses and occupant discomfort. Thatis, the thermostat does not really do anything to account for where theuser is located when determining whether climate controls should beactive or inactive.

Aside from the thermostat existing in a poor location to accuratelydetermine comfort, the accuracy of the sensors used in industry standardthermostat lacks the accuracy and sensitivity to allow for virtualizedmodeling. Basic knowledge of process control uses proportional,integral, and derivative (PID) algorithms where the derivative or secondorder portion of the control is omitted. This omission may be the resultof most on/off type control strategies having no way (i.e., lackingsufficient hardware) to accurately apply the derivative. Additionally oralternatively, the second order portion may be omitted because majormanufactures of modern programmable thermostats implement inexpensivecomponents and resistive temperature detector (RTDs) which may not carrythe bit resolution to achieve a more accurate control of +/−2 degreesFahrenheit as tested, or less accuracy with previous generationbimetal/mercury type sensors. Aside from cost and complexity, currentthermostats, capable of accurate readings and responding to an accuratereading may lead to over cycling mechanical equipment.

As illustrated in FIG. 7, the virtualized thermostat is comprised of 1to 5 sensor types with an accuracy of approximately +/−0.75 degreeFahrenheit at 75.degree. F. and occupancy sensor inputs with outputsdefined by heating, ventilating, cooling, humidification,dehumidification, and fan flow (air volume). As used in thespecification and the claims, the term approximately shall mean that thevalue is within 10% of the stated value, unless otherwise specified.

Input sensors and their function number:

#1. Temperature sensor at thermostat location 703—If we know that allthe conditioned areas communicate with this location, the sensor will beused to determine the basic envelope and mechanical system performance.By measuring the outside air temperature 704 and “stressing” the systemat design conditions with known system delivery capacity, it can bedetermined from the 2nd order drift and acceleration to set point, theactual heating cooling load experienced by the structure. In math terms,2nd order acceleration is one factor in the proposed Bayes's predictorof anticipated performance.

#2 Outdoor air temperature sensor 704—Research has indicated that microclimates exist so close to structures that the weather data fed from anysource other than the structure itself is meaningless. An accurateoutdoor air sensor can be located approximately 4′-0″ above grade andaway from any heat source or radiant influence. Again, the temperaturesmeasured by the acceleration of change is the basis of predictedperformance.

#3 Supply air temperature 705—The supply air temperature is required toknow the system status and performance. Supply air temperature,thermostat (cumulative), and outside air temperature, along with fanflow data, can provide a virtualized energy consumption profile that canbe a subdivided energy set as used as a comparator to actual name plateloads. On systems with mechanical cooling, a sensor on the condensingunit provides the refrigeration efficiency feedback.

#4 Radiant sensor 706—Many buildings employ temperature setbackstrategies to save energy. Temperatures in spaces in the building driftto near ambient conditions when the building is not occupied. Whenoccupation reoccurs mechanical systems, which were designed for steadystate load conditions, are now forced to overcome the mass load. Thetime the mechanical system is running and not meeting the thermostat setpoint as “unmet hours”. Many times spaces are intentionally orunintentionally overheated or over cooled to compensate for thiscondition. Studies have indicated that occupant comfort is a compilationof thermal range and maintaining a radiant mean temperature, or thatcondition where the radiant temperature of surfaces within the spaceneither add to nor remove radiant effects to the occupant's body. Theradiant sensor described here and illustrated in FIG. 8 is a framemounted to an exterior wall 801 that senses using an Infrared (IR)sensor, the outside wall internal surface temperature 802.Simultaneously, a thermocouple can sample surrounding air temperature803 (isolated from the wall using an insulated frame, 804) and an IRsensor to measure the IR temperature of the surroundings 805. Thissensor provides information to our virtualized thermostat and providesfeedback in creating radiant mean conditions.

#5 Occupancy sensors 707—An ear, wrist, lapel, or smart device that is aconnected device running an enabled energy service would register withthe localized thermostat or some other wireless enabled device to senddata to the cloud based virtualized thermostat. As each device registerswith the virtualized thermostat the represents the localized thermostat,the space becomes “occupied”. Similar to illustration of FIG. 8, eachdevice may be capable of reading surface or skin temperature and includean IR sensor to read surrounding temperature.

Occupancy sensors will establish occupant conditions and individualpreferences will be uploaded to the virtualized thermostat. As usersmove around the space, the thermostat will compensate to accommodateusers' preferences. And as new users join the sensor network, theirpreferences and sensed conditions are pooled within the virtualizedthermostat increasing accuracy of the model. Straight polling orhierarchy of users can establish localized space temperature set pointsdepending on individual set points and associated dead band preferences,energy use/cost can be apportioned over all users present.

Virtualized thermostat outputs 708—By the nature of the software basedvirtualized model, the device is capable of outputting: step on/off,staged, linear sequenced, analog or any combination thereof. As shown inFIG. 2, between the sensors and output configuration is a modular blockof objects to represent: (1) a structure, or envelope block, (2) anoccupant block and (3) a predictive block, resulting in a responsiveload variable.

The inputs 702 and outputs 708, identified within a specific consumptionmodel, represent a virtualized field bus (VFB). The VFB in itsvirtualized form represents normalized values that allow for consistentrepresentation and as a means of creating a scalable virtualizedplatform. Values within the VFB can be real, representations of realdata or calculated.

Once inputs and outputs from the VFB are normalized, they are preparedto input to various portions of the control block diagram as representedin FIG. 3. For instance, the “structure block” class 301 as identifiedin FIG. 3 shows a list of variables that impact both the performance andthe predicted performance of a thermostat/structure relationship.“Block” is defined as the set of equations or algorithms related to aspecific step from input to response, IE structure block 301, user block302, and predictor block

The static and dynamic models are made up of segments within each blockof calculations. The static model 201 begins each event horizon bystarting the process with a stored variable. In Bayes's terms, this isthe first predictor that was calculated from the original design modelor as each event horizon occurs, is a modified value. What is passedbetween segment block variables is a simple numeric or enumerated statevariable with a Laplace type matrix stored database. The dynamic model202 and static model 201 each maintain a separate database. It isanticipated in some consumption models that multiple types of databasesof each type will need to be maintained in order to describe thecomplexity of each model.

The dynamic model 202 is a closed loop that responds directly to thephysical variables 204 or modified variables 205. The predictive datainterface 206 is the connection between other virtualized devices andthe dynamic model 202. In order for other virtualized devices to affectchange within the dynamic model, the predictive interface 206 modifies avariable 208 within the static model 201 and the static model creates avariation state 209 within the modified variables 205. It is understoodthat the modified variable may or may not affect change immediately inreal time to the dynamic model, but may change the prediction of how thesystem may respond in the future.

For example, the time of day for an event was modified a day in advanceand in this case the dynamic model is unaffected and continues toperform as expected. If on the other hand, the static model changes theresponse state from “on” to “off”. Before this change occurs, thedynamic model notes the change in state, reports this change to thepredictive data interface, writes the new variable to the modifiedvariables and changes the state to “off”. One skilled in the art willappreciate that, a control loop could be modified to a lesser extentwithout an immediate “on” to “off” switch, but still bring about achange when you consider the adjustment scaled over a largeinfrastructure.

It is also possible, for example, that the dynamic model's state ischanged from “on” to “off” by change at the physical device's switch.The static model tracks as a comparison model that predicts possibleresponses and variations between the static model and dynamic modelrepresent measured performance within the transactional model level.

The virtualized thermostat reads very accurate input data, which it usesin predictive ways, but the actual response output is tuned and temperedby Bayesian interpretation algorithms and factors particular to theindividual consumption instance. Solving mixed integer nonlinearequations using second order variables within a scalable model withinput from local and networked sources and passing both complex and“simplified user understood data” requires a two model approach in acloud based managed solution where one model keeps track of how thesystem operates in the physical world and the second model communicateswith other virtualized devices and computes predicted responses andtracks comparisons.

Benefits of Implementation 1:

By its very nature, a virtualized thermostat can accept input from alltypes of sources and respond in a complex way, according to specificallytuned and/or predictive (Bayes's) responses.

A virtualized scalable model can contain information that can bepresented in any form necessary to interface with simple humaninteraction or complex machine to machine interface. This allows forsimple local user interface with complex control algorithms handled offsite in the cloud environment.

By placing highly accurate sensors, it is not only necessary, butpossible to enable the use of 2nd order equations to represent complexvirtualized interactions and behaviors.

By using a sensor that reads both surface temperature and localizedsurrounding temperature, more accurate comfort decisions can be made toimprove comfort and reduce energy consumption.

By placing consumption devices into a single virtualized model, it ispossible to make small unnoticeable adjustments to individual controlloops and group these changes to bring about large systemsynchronizations to control demand response and power factor loading.

By placing consumption devices into a virtualized model and using sensorinput data, it is possible to apportion consumption from a singlemechanical system or single consumption appropriately across multipleusers.

By placing consumption devices into a virtualized model, it is possibleto cross connect apportioned consumption of individual utility sourcetypes to individual virtualized consumption, based on user's preferencesin real time.

Second Implementation—Utility Grid

This invention presents a novel approach to revise the currentelectrical grid delivery by using a virtualized model to segregategenerational sources into separate generational types (solar, wind,hydro, coal, biomass, etc.) and deliver this to the consumer in such away as to allow an open market bid on each unique generation type inreal time and to identify all delivery costs associated with eachparticular generation type and its relative cost to deliver to aparticular user. Accurate pricing in a multi-tiered form that representsall generational costs driven by eco social market forces will enablethe possibility of a distributed power grid architecture.

As illustrated in FIG. 4 the object of this implementation is thevirtualization of generation sources into unique energy types andidentify all attributes of each type relative to its source,environmental costs (water use, CO2 production, sulfur/NOx emissions,etc.) transmission costs/losses, and inter-operator exchange rates, topresent consumers a tiered buying market approach. The generation block401 contains the variables needed to describe a generation source, anddelivery specific block 402 describes variables involved is getting thegenerated capacity to the point of distribution. The predictor block403, describes the ability of any particular generation source toperform under certain loading conditions.

This implementation has two virtualized consumption models: one static201, and one dynamic 202. The static model is the predictive model andinterfaces with other virtualized devices using the predictive datainterface 206 and the dynamic model interfaces directly physical devicesin real time through the virtualized field bus 204 and 207.

Users 508 that participate in grid market pricing are presented a webinterface 506 or some other connected device that identifies locationswithin the grid and a multi-tiered structure pricing. This informationis then fed into the static virtualized consumption model 507. Resourcesare then aggregated within the market transaction static consumptionmodel 501 and requests are sent via the ISO interface 505.

Within the virtualized consumption model 507, the user's past andpresent activities are tracked to incorporate them into the predictivemodel and requested electrical generation source and allocation ofresource to be fed to the market transaction level 501.

For example, as shown in FIG. 6, the market transaction level matchesuser source requests 603 from multi-tiered pricing and actualvirtualized consumption 602 against individual generation source typesand costing values 605. Closure between the aggregated consumptionrequests 603 for each source type and the corresponding generationalresponse 605 at each source type to is used to create a tiered pricingmodel 607 in real time.

User preferences within the static model will prioritize generationsource demands and adjust pricing according to allocation of service inreal time. Dynamic data fed back from the generation model willdetermine if dispatched generation has been consumed by the appropriateuser.

This implementation provides closure between generation and consumptionfor any individual electric power source and to allow participation ofgeneration and consumers at any level of grid traffic. Additionally oralternatively, this implementation allows consumers to set pricing andallow bidding for selected electrical generation sources.

Like other levels of the virtualized model, the virtualized generationlevel 502 will hold two models: one dynamic and one static. The staticmodel will present generational source request fed from the markettransaction level to ISO's 504 and other grid participants, eitherremote 505 or local 503. The dynamic virtualized model will verifygenerational sources and actual costs in real time. The comparisonbetween these two model values for each source will feed back realincremental costing data to the market transaction level. Although somevariation of this invention could be developed using multi-tier conceptsor abstraction layer techniques, in order to combine all functions intoa single model or some variation thereof, this novel concept of usingtwo comparable models would be emulated.

This implementation allows ISOs 505 to continue to control and dispatchelectrical power to the grid but access resources in the grid accordingto consumer demand for a particular generated source; either by type orlocation. Additionally or alternatively, this implementation allowsparticipants to generate power and those not currently tracked by ISOs503 are given real time source and cost consideration over the entiregrid virtualized model.

It is understood that some generational sources such as wind and solarcreate fluctuations in the grid and the fluctuations are handled in realtime within the dynamic virtualized generational model. And ancillarysources are created through software to compensate and balance theadverse effects to power loading by adjusting consumption through thevirtualized consumption model. This information in turn is fed back tothe market transaction level to reestablish allocation of services andpricing levels in real time.

The virtualized generational model will identify all costs associatedwith generation and delivery in order to normalize all costs to dispatchelectrical energy from any particular source to any selected consumer.This would include such costs as carbon sequestration for fossil fuelsand radioactive disposal/storage for nuclear generated power.

Benefits of Implementation

A virtualized model can contain information that can be presented in anyform necessary to interface with simple human interaction or complexmachine to machine interface. This allows for simple local userinterface with complex control algorithms handled off site in the cloudenvironment.

By placing generation devices into a single virtualized model, it ispossible to make small changes within each generational source andrelated ancillary devices to affect demand response and power factorloading.

By placing generational devices into a single virtualized model andusing input data, it is possible to identify and segregate multiplegenerational source types, either remote or local, into a uniquegeneration type and allocate the total across multiple users in realtime.

By placing generational devices into a single virtualized model, it ispossible to identify all costs associated with creating electricalenergy and delivery to any point within the grid. This would includeboth short term and long term environmental costs.

By placing generational devices into a single virtualized model, it ispossible to create a tiered pricing structure and allow users/consumersto interface with pricing and generational information that is pertinentto that user.

By placing generational devices into a single virtualized model, it ispossible to allow users/consumers to create social groups to affectpricing and demand for specific types/locations of generational sources.

FIG. 9, and the following discussion, are intended to provide a brief,general description of a suitable computing environment in which theinvention may be implemented. Although not required, the invention willbe described in the general context of computer-executable instructions,such as program modules, being executed by computers in networkenvironments. Generally, program modules include routines, programs,objects, components, data structures, etc. that performs particulartasks or implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

One of skill in the art will appreciate that the invention may bepracticed in network computing environments with many types of computersystem configurations, including personal computers, hand-held devices,mobile phones, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. The invention may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination of hardwired or wirelesslinks) through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

With reference to FIG. 9, an example system for implementing theinvention includes a general purpose computing device in the form of aconventional computer 920, including a processing unit 921, a systemmemory 922, and a system bus 923 that couples various system componentsincluding the system memory 922 to the processing unit 921. It should benoted however, that as mobile phones become more sophisticated, mobilephones are beginning to incorporate many of the components illustratedfor conventional computer 920. Accordingly, with relatively minoradjustments, mostly with respect to input/output devices, thedescription of conventional computer 920 applies equally to mobilephones. The system bus 923 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. The system memoryincludes non-volatile memory 924 and random access memory (RAM) 925. Abasic input/output system (BIOS) 926, containing the basic routines thathelp transfer information between elements within the computer 920, suchas during start-up, may be stored in memory 924.

The computer 920 may also include a magnetic hard disk drive 927 forreading from and writing to a magnetic hard disk 939, a solid statedrive 928 for reading from or writing to a removable memory card 929,and an optical disc drive 930 for reading from or writing to removableoptical disc 931 such as a CD-ROM or other optical media. The magnetichard disk drive 927, magnetic disk drive 928, and optical disc drive 930are connected to the system bus 923 by an interface 932, a magnetic diskdrive-interface 933, and an optical drive interface 934, respectively.The drives and their associated computer-readable media providenonvolatile storage of computer-executable instructions, datastructures, program modules and other data for the computer 920.Although the exemplary environment described herein employs a magnetichard disk 939, a removable memory card 929 and a removable optical disc931, other types of computer readable media for storing data can beused, including magnetic cassettes, removable magnetic memory disks,digital versatile discs, Bernoulli cartridges, RAMs, ROMs, and the like.

Program code means comprising one or more program modules may be storedon the hard disk 939, memory card 929, optical disc 931, non-volatilememory 924 or RAM 925, including an operating system 935, one or moreapplication programs 936, other program modules 937, and program data938. A user may enter commands and information into the computer 920through keyboard 940, pointing device 942, or other input devices (notshown), such as a microphone, push button, touch pad, thermal sensors,scanner, motion detectors or the like. These and other input devices areoften connected to the processing unit 921 through a serial portinterface 946 a coupled to system bus 923. Alternatively, the inputdevices may be connected by other interfaces, such as a parallel port,Bluetooth port 946 b or a universal serial bus (USB) 946 c. A monitor947 or another display device is also connected to system bus 923 via aninterface, such as video adapter 948. In addition to the monitor,personal computers typically include other peripheral output devices(not shown), such as speakers and printers.

The computer 920 may operate in a networked environment using logicalconnections to one or more remote computers, such as remote computers949 a and 949 b. Remote computers 949 a and 949 b may each be anotherpersonal computer, a server, a router, a network PC, a peer device orother common network node, and typically include many or all of theelements described above relative to the computer 920, although onlymemory storage devices 950 a and 950 b and their associated applicationprograms 936 a and 936 b have been illustrated in FIG. 9. The logicalconnections depicted in FIG. 9 include a local area network (LAN) 951and a wide area network (WAN) 952 that are presented here by way ofexample and not limitation. Such networking environments are commonplacein office-wide or enterprise-wide computer networks, intranets and theInternet.

When used in a LAN networking environment, the computer 920 can beconnected to the local network 951 through a network interface oradapter 953. When used in a WAN networking environment, the computer 920may include a modem 954, a wireless link, or other means forestablishing communications over the wide area network 952, such as theInternet. The modem 954, which may be internal or external, is connectedto the system bus 923 via the serial port interface 946. In a networkedenvironment, program modules depicted relative to the computer 920, orportions thereof, may be stored in the remote memory storage device. Itwill be appreciated that the network connections shown are exemplary andother means of establishing communications over wide area network 952may be used.

As used in the specification and the claims, the phrase “configured to”denotes an actual state of configuration that fundamentally ties recitedelements to the physical characteristics of the recited structure. As aresult, the phrase “configured to” reaches well beyond merely describingfunctional language or intended use since the phrase actively recites anactual state of configuration.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed:
 1. A virtual thermostat for virtualizing energyfunctions and processes, the virtual thermostat comprising: a sensorarray, wherein the sensor array includes: an interior temperature sensorat a thermostat location, wherein the interior temperature sensor isconfigured to measure the temperature within an interior space; anoutdoor temperature sensor, wherein the outdoor temperature sensor isconfigured to measure the temperature sensor of an outdoor location inproximity to the interior space; and a supply air temperature sensor,wherein the supply air temperature sensor is configured to measure thetemperature of conditioned air that is being supplied to the interiorspace; one or more computer readable media, wherein the one or morecomputer readable media contain a set of computer-executableinstructions to be executed by the logic device, the set ofcomputer-executable instructions configured to: receive input from thesensory array; create a unique representation of the physical systemscontrolled; create a unique data set of the virtualized systems andconnect this data set to other networked devices using machine tomachine interaction; allow the virtualized thermostat to share simpleuser data and user requests with other consumption or generationalmodels; allow the data set representing the virtualized thermostat toexist outside of the controlled system; and employ sophisticated controllogic/algorithms to output one or more control signals designed toaffect the desired conditioning.
 2. The virtual thermostat of claim 1,wherein the sensor array further includes: a radiant temperature sensor,wherein the radiant temperature sensor: is configured to measure theradiant temperature of a surface within the interior space; andincludes: a frame mounted to an exterior wall that senses using an IRsensor, the outside wall internal surface temperature; a thermocouplesampling surrounding air; and includes an IR sensor to measure the IRtemperature of the surroundings; is isolated from the wall using aninsulated frame.
 3. The virtual thermostat of claim 1, wherein thesensor array further includes an occupancy sensor, wherein the occupancysensor is configured to establish occupant conditions.
 4. The system ofclaim 1, wherein each of the sensors in the sensor array provide datathat will allow the virtual thermostat to measure 2nd order equationresponses in order to predict the system performance as the basis of thevirtualized consumption model.
 5. The virtual thermostat of claim 1,wherein the sensor array further includes: a sensor configured to: readsurface temperature of the structure it is mounted upon using infraredmeans; and, read the surrounding temperature using infrared means; and,read the surrounding temperature using an air sensor type thermocouple;and forward data to a virtualized thermostat as described to moreaccurately define space conditions resulting in the optimized thermalcomfort for the least energy cost.
 6. The virtual thermostat of claim 5,wherein the sensor includes at least one of: an ear device; a wristdevice; a lapel device; or a smart device that is connected to thevirtual thermostat using Bluetooth standard.
 7. The virtual thermostatof claim 6, wherein the sensor is configured to: register with thevirtualized thermostat to identify multiple occupants, wherein theregistration process would allow the virtualized thermostat to receivethe occupant's consumption preferences and variables and track multipleoccupants with the total energy use, source, and cost apportioned overeach occupant.